





'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 							__SCREAMING_SNAKE_CASE    (lowerCamelCase_			,				lowerCamelCase_			,				unittest.TestCase ):






				"""simple docstring"""







				__a  =IFInpaintingSuperResolutionPipeline
				__a  =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
				__a  =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
				__a  =PipelineTesterMixin.required_optional_params - {'latents'}




				def     UpperCamelCase__  (    self	:   Dict   ):
								return self._get_superresolution_dummy_components()




				def     UpperCamelCase__  (    self	:   List[Any]   ,							__a	:   Dict   ,							__a	:   Any=0   ):


								if str(__a   ).startswith("mps"   ):
												_a				      =					torch.manual_seed(__a   )
								else:
												_a				      =					torch.Generator(device=__a   ).manual_seed(__a   )

								_a				      =					floats_tensor((1, 3, 16, 16)   ,							rng=random.Random(__a   )   ).to(__a   )
								_a				      =					floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(__a   )   ).to(__a   )
								_a				      =					floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(__a   )   ).to(__a   )

								_a				      =					{
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": image,
								    "original_image": original_image,
								    "mask_image": mask_image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs




				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()   ,							reason="XFormers attention is only available with CUDA and `xformers` installed"   ,							)
				def     UpperCamelCase__  (    self	:   Union[str, Any]   ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




				def     UpperCamelCase__  (    self	:   Tuple   ):
								self._test_save_load_optional_components()




				@unittest.skipIf(torch_device != "cuda"   ,							reason="float16 requires CUDA"   )
				def     UpperCamelCase__  (    self	:   Union[str, Any]   ):
								super().test_save_load_floataa(expected_max_diff=1e-1   )




				def     UpperCamelCase__  (    self	:   int   ):
								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




				def     UpperCamelCase__  (    self	:   Tuple   ):
								self._test_save_load_local()




				def     UpperCamelCase__  (    self	:   int   ):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2   ,							)




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


